Identifiable attribution maps with contrastive learning for mapping neurons to neural latent dynamics
Steffen Schneider, Anastasiia Filippova, Rodrigo González Laiz, Markus Frey, Mackenzie W Mathis · FENS Forum 2024
Quantifying the contribution of dynamically changing neural activity to goal-directed behaviors is a longstanding problem in neuroscience. One approach is to systematically test how behaviors map to individual firing rates at each time step, but it is less clear how behaviors map to complex neural latent dynamics. In recent years deep learning approaches have become powerful tools to extract these latent dynamics (Schneider, Lee, Mathis 2023), yet how to probe them in order to attribute input neurons to latents remains challenging. Gradient-based attribution is one of the methods that aim to e
ePoster · Neuroscience